Solid Waste Management Scenario in India and Illegal Dump Detection Using Deep Learning: An AI Approach towards the Sustainable Waste Management

Abstract

The study is presented in four sections. The first section defines the municipal solid waste and solid waste management system. The second section illustrates the descriptive statistical analysis of waste generation patterns in India. The average waste generation in India was 160,038.9 tons per day in 2021; 95% of this total waste was collected and transported to the disposal sites. Based on scientific studies and observations, the per capita waste generation rate in 2018 was 0.490–0.626 g per day. In the last one and a half decades (1999–2000 to 2015–2016), Delhi and Bangalore have shown the highest percentage growth of 2075% and 1750%, respectively, in total waste generation among the highest population cities. The analysis of waste generation patterns concludes urbanization is a major factor that highly influences the waste generation rate. The third section describes the major issues in current solid waste management services. Some of these issues are the unavailability of web portals for citizens, no real-time monitoring of bins, collection vehicles and illegal dumping. These issues are identified based on the survey performed in a city and analysis of related research studies and scientific reports. We determined that illegal dumping is one of these major concerns and needs a technological solution. In the fourth section, we propose a multipath convolutional neural network (mp-CNN) to detect and localize the waste dumps on streets and roadsides. We constructed our dataset to train and test the proposed model, as no benchmark dataset is publicly available to obtain this objective. We applied the weakly supervised learning approach to training the model. In this approach, mp-CNN was trained according to the image class; in our case, it is two (waste and non-waste). In the testing phase, the model showed the performance evaluation matrices 97.82% of precision, 98.86% of recall, 98.34% of F1 score, 98.33% of accuracy, and 98.63% of AUROC for this binary classification. Due to the scarcity of benchmark datasets, waste localization results cannot be presented quantitatively. So, we performed a survey to compare the overlapping of the mask generated by the model with the region waste in the actual image. The average score for the generated mask obtained a score of 3.884 on a scale of 5. Based on the analysis of model performance evaluation parameters, precision-recall curve, receiver characteristic operator curve, and comparison of mask generated by the model over waste with corresponding actual images show that mp-CNN performs remarkably good in detection, classification, and localization of waste regions. Finally, two conceptual architectures in the context of developing countries are suggested to demonstrate the future practical applications of the mp-CNN model.

Artificial intelligence for waste management in smart cities: a review

“…Shahab and Anjum ( 2022 ) used a multipath convolutional neural network model to identify and localize waste areas to identify illegal dumping. Because no data set for illegal dumping was identified in this model, Shahab and Anjum ( 2022 ) devised a quantitative investigation procedure.…”

“…Shahab and Anjum ( 2022 ) used a multipath convolutional neural network model to identify and localize waste areas to identify illegal dumping. Because no data set for illegal dumping was identified in this model, Shahab and Anjum ( 2022 ) devised a quantitative investigation procedure. After training a multipath convolutional neural network model with 9000 pictures, the researchers used 3000 additional pictures to test the model’s classification accuracy, which was found to be 98.33%.…”

“…Output result represents the result of artificial intelligence recognition. YOLO is the “you only look once” model algorithm Input data Types of artificial intelligence Output result Reference Pictures with or without waste Multipath convolutional neural network model Determine if the vehicle is dumping garbage Shahab and Anjum ( 2022 ) Picture of vehicle YOLO Determine if the vehicle is dumping garbage Du ( 2020 ) Images from the drone Faster regions with convolutional neural network features Identify the location of garbage Takahashi et al ( 2022 ) Pictures of the dump and the garbage Openpose and YOLO Determine if the dumper is dumping Kim and Cho ( 2022 ) Security camera shot of the street Faster regions with convolutional neural network features Determine if the street is clean Thotapally ( 2022 ) Images from the drone Single-shot detector algorithm Identify the location of the waste Youme et al ( 2021 ) High-resolution aerial images Basic convolutional neural network classification model Identify illegal dumping sites Padubidri et al ( 2022 ) High-resolution aerial images Residual block classification model Identify illegal dumping sites Padubidri et al ( 2022 ) High-resolution earth observation Random forest algorithm Identify the location of the waste Ulloa-Torrealba et al ( 2023 ...…”

Waste management developments in the last five decades: Asian perspective

“…In the following two decades, the quantity of MSW generated has increased even further. In 2021, this had increased to approximately 160,038 TPD (Shahab and Anjum, 2022). Also, India has significantly changed waste management practices over the last five decades.…”

A review of barriers to circular economy implementation in solid waste management

“…Population growth, unsustainable consumption patterns and the current pro t-driven linear economic systems result in uncontrolled solid waste production (Malinauskaite et al, 2017). The world's waste generation is expected to increase by 70% by 2025 (Shahab & Anjum, 2022) and solid waste-related emissions are expected to total 2.38 billion tonnes of CO 2 equivalent by 2050 (Nkonya, 2022). This has the potential to cause serious environmental, economic, and social implications (Behrooznia et al, 2020;Istrate et al, 2020; Urban & Nakada, 2021).…”